Python preprocessing.StandardScaler类代码示例

def check_transformer_pickle(name, Transformer):
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    n_samples, n_features = X.shape
    X = StandardScaler().fit_transform(X)
    X -= X.min()
    # catch deprecation warnings
    with warnings.catch_warnings(record=True):
        transformer = Transformer()
    if not hasattr(transformer, 'transform'):
        return
    set_random_state(transformer)
    set_fast_parameters(transformer)

    # fit
    if name in CROSS_DECOMPOSITION:
        random_state = np.random.RandomState(seed=12345)
        y_ = np.vstack([y, 2 * y + random_state.randint(2, size=len(y))])
        y_ = y_.T
    else:
        y_ = y

    transformer.fit(X, y_)
    X_pred = transformer.fit(X, y_).transform(X)
    pickled_transformer = pickle.dumps(transformer)
    unpickled_transformer = pickle.loads(pickled_transformer)
    pickled_X_pred = unpickled_transformer.transform(X)

    assert_array_almost_equal(pickled_X_pred, X_pred)

def check_classifiers_classes(name, Classifier):
    X, y = make_blobs(n_samples=30, random_state=0, cluster_std=0.1)
    X, y = shuffle(X, y, random_state=7)
    X = StandardScaler().fit_transform(X)
    # We need to make sure that we have non negative data, for things
    # like NMF
    X -= X.min() - .1
    y_names = np.array(["one", "two", "three"])[y]

    for y_names in [y_names, y_names.astype('O')]:
        if name in ["LabelPropagation", "LabelSpreading"]:
            # TODO some complication with -1 label
            y_ = y
        else:
            y_ = y_names

        classes = np.unique(y_)
        # catch deprecation warnings
        with warnings.catch_warnings(record=True):
            classifier = Classifier()
        if name == 'BernoulliNB':
            classifier.set_params(binarize=X.mean())
        set_fast_parameters(classifier)
        # fit
        classifier.fit(X, y_)

        y_pred = classifier.predict(X)
        # training set performance
        assert_array_equal(np.unique(y_), np.unique(y_pred))
        if np.any(classifier.classes_ != classes):
            print("Unexpected classes_ attribute for %r: "
                  "expected %s, got %s" %
                  (classifier, classes, classifier.classes_))

    def clustering_approach(self):
        '''
        Cluster user data using various clustering algos
        IN: self.df_full and self.labels
        OUT: results to stdout
        '''
        print 'Fitting clustering model'
        X = self.df_full.values
        y = self.labels

        # scale data
        scaler = StandardScaler()
        X = scaler.fit_transform(X)

        # KMeans
        km_clf = KMeans(n_clusters=2, n_jobs=6)
        km_clf.fit(X)

        # swap labels as super-users are in cluster 0 (messy!!)
        temp = y.apply(lambda x: 0 if x == 1 else 1)
        print '\nKMeans clustering: '
        self.analyse_preds(temp, km_clf.labels_)

        # Agglomerative clustering
        print '\nAgglomerative clustering approach: '
        ac_clf = AgglomerativeClustering()
        ac_labels = ac_clf.fit_predict(X)
        self.analyse_preds(y, ac_labels)

        return None

def buildCoordinationTreeRegressor(predictorColumns, element, coordinationDir = 'coordination/', md = None):
    """
    Build a coordination predictor for a given element from compositional structure data of structures containing that element. Will return a model trained on all data, a mean_absolute_error score, and a table of true vs. predicted values
    """
    try:
        df = pd.read_csv(coordinationDir + element + '.csv')
    except Exception:
        print 'No data for ' + element
        return None, None, None
    df = df.dropna()
    if('fracNobleGas' in df.columns):
        df = df[df['fracNobleGas'] <= 0]
    
    if(len(df) < 4):
        print 'Not enough data for ' + element
        return None, None, None
    s = StandardScaler()
    
    X = s.fit_transform(df[predictorColumns].astype('float64'))
    y = df['avgCoordination'].values

    rfr = RandomForestRegressor(max_depth = md)
    acc = mean(cross_val_score(rfr, X, y, scoring=make_scorer(mean_absolute_error)))

    X_train, X_test, y_train, y_test = train_test_split(X,y)
    rfr.fit(X_train,y_train)
    y_predict = rfr.predict(X_test)
    
    t = pd.DataFrame({'True':y_test, 'Predicted':y_predict})
    
    rfr.fit(X, y)

    return rfr, t, round(acc,2)

def train_and_test(train_books, test_books, train, scale=True):
    X_train, y_train, cands_train, features = get_pair_data(train_books, True)
    X_test, y_test, cands_test, features = get_pair_data(test_books)

    scaler = None
    if scale:
        scaler = StandardScaler()
        X_train = scaler.fit_transform(X_train)
        X_test = scaler.transform(X_test)

    print sum(y_train)*0.1/len(y_train)
    print 'Start training'
    print X_train.shape
    clf = train(X_train, y_train)
    print 'Done training'
    y_train_pred = clf.predict(X_train)
    y_test_pred = clf.predict(X_test)

    '''
    # print performance for training books
    print "--------------Traning data-------------"
    train_perf = evaluate_books(clf, train_books, scaler, evaluate_pair)

   # print performance for testing books
    print "\n"
    print "--------------Testing data-------------"
    test_perf = evaluate_books(clf, test_books, scaler, evaluate_pair)
    '''
    print 'Train Non-unique Precision:', precision(y_train_pred, y_train), 'Non-unique Recall:', recall(y_train_pred, y_train)
    print 'Test Non-unique Precision:', precision(y_test_pred, y_test), 'Recall:', recall(y_test_pred, y_test)
    return clf, scaler, X_train, y_train, X_test, y_test

def load_train_data(path):
    print("Loading Train Data")
    df = pd.read_csv(path)
    
    
    # Remove line below to run locally - Be careful you need more than 8GB RAM 
    rows = np.random.choice(df.index.values, 40000)
    df = df.ix[rows]
    # df = df.sample(n=40000)
    # df = df.loc[df.index]
    
    labels = df.target

    df = df.drop('target',1)
    df = df.drop('ID',1)
    
    # Junk cols - Some feature engineering needed here
    df = df.fillna(-1)

    X = df.values.copy()
    
    np.random.shuffle(X)

    X = X.astype(np.float32)
    encoder = LabelEncoder()
    y = encoder.fit_transform(labels).astype(np.int32)
    scaler = StandardScaler()
    X = scaler.fit_transform(X)
    return X, y, encoder, scaler

def knn(x_train, y_train, x_valid):
    x_train=np.log(x_train+1)
    x_valid=np.log(x_valid+1)

    where_are_nan = np.isnan(x_train)
    where_are_inf = np.isinf(x_train)
    x_train[where_are_nan] = 0
    x_train[where_are_inf] = 0
    where_are_nan = np.isnan(x_valid)
    where_are_inf = np.isinf(x_valid)
    x_valid[where_are_nan] = 0
    x_valid[where_are_inf] = 0

    scale=StandardScaler()
    scale.fit(x_train)
    x_train=scale.transform(x_train)
    x_valid=scale.transform(x_valid)

    #pca = PCA(n_components=10)
    #pca.fit(x_train)
    #x_train = pca.transform(x_train)
    #x_valid = pca.transform(x_valid)

    kneighbors=KNeighborsClassifier(n_neighbors=200,n_jobs=-1)
    knn_train, knn_test = stacking(kneighbors, x_train, y_train, x_valid, "knn")
    return knn_train, knn_test, "knn"

def normalize( training_data, test_data ):
	scaler = StandardScaler()
	values = scaler.fit_transform( training_data )
	training_data = pd.DataFrame( values, columns=training_data.columns, index=training_data.index )
	values = scaler.transform( test_data )
	test_data = pd.DataFrame( values, columns=test_data.columns, index=test_data.index )
	return training_data, test_data 

def run_model( model, model_name, X, Y, X_val):

    new_values = [ [x] for x in range(len(X))]
    X = numpy.append(X, new_values, 1)
    from sklearn.preprocessing import StandardScaler # I have a suspicion that the classifier might work better without the scaler
    scaler = StandardScaler().fit(X)
    X = scaler.transform(X)
    max_time_val = X[-1][-1] *2 - X[-2][-1]

    Y = make_black_maps_class(Y)
    # Load validation data
    model.fit(X, Y)

    new_values = [ [max_time_val] for x in range(len(X_val))]
    X_val = numpy.append(X_val, new_values, 1)

    # Now predict validation output
    Y_pred = model.predict(X_val)

    # Crop impossible values
    Y_pred[Y_pred < 0] = 0
    Y_pred[Y_pred > 600] = 600

    savetxt('final_pred_y{0}.csv'.format(model_name), Y_pred, delimiter=',')

    black_map_count = 0
    for y in Y_pred:
        if y == 600:
            black_map_count += 1

    print black_map_count, model_name
    sys.stdout.flush()

def load_data_csv_advanced(datafile):
    """
    Loads data from given CSV file. The first line in the given CSV file is expected to be the names of the columns.
    :param datafile: path of the file
    :return: a NumPy array containing a data point in each row
    """

    # File format for CSV file. For example, setting _X_COLUMN to 'x' means that x coordinates of geographical location
    # will be at the column named 'x' in the CSV file.
    _COLUMN_X = 'x'
    _COLUMN_Y = 'y'

    data = pd.read_csv(datafile)

    # Normalize
    scaler = StandardScaler()
    scaler.fit(data[[_COLUMN_X, _COLUMN_Y]])
    data[[_COLUMN_X, _COLUMN_Y]] = scaler.transform(data[[_COLUMN_X, _COLUMN_Y]])

    #  Get feature vector names by removing "x" and "y"
    feature_vector_names = data.columns.difference([_COLUMN_X, _COLUMN_Y])
    data_coords = data[[_COLUMN_X, _COLUMN_Y]].values

    result = {"coordinates": data_coords}

    for feature in feature_vector_names:
        data_words = [[e.strip() for e in venue_data.split(",")] for venue_data in data[feature].values.flatten().tolist()]

        result[feature] = data_words

    return sparsify_data(result, None, None), scaler  # None for both params since SVD is not used

def lassoRegression(X,y):

    print("\n### ~~~~~~~~~~~~~~~~~~~~ ###")
    print("Lasso Regression")

    ### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
    myDegree = 40
    polynomialFeatures = PolynomialFeatures(degree=myDegree, include_bias=False)
    Xp = polynomialFeatures.fit_transform(X)

    myScaler = StandardScaler()
    scaled_Xp = myScaler.fit_transform(Xp)

    ### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
    lassoRegression = Lasso(alpha=1e-7)
    lassoRegression.fit(scaled_Xp,y)

    ### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
    dummyX = np.arange(0,2,0.01)
    dummyX = dummyX.reshape((dummyX.shape[0],1))
    dummyXp = polynomialFeatures.fit_transform(dummyX)
    scaled_dummyXp = myScaler.transform(dummyXp)
    dummyY = lassoRegression.predict(scaled_dummyXp)

    outputFILE = 'plot-lassoRegression.png'
    fig, ax = plt.subplots()
    fig.set_size_inches(h = 6.0, w = 10.0)
    ax.axis([0,2,0,15])
    ax.scatter(X,y,color="black",s=10.0)
    ax.plot(dummyX, dummyY, color='red', linewidth=1.5)
    plt.savefig(filename = outputFILE, bbox_inches='tight', pad_inches=0.2, dpi = 600)

    ### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
    return( None )

def load_data_csv(datafile):
    """
    Loads data from given CSV file. The first line in the given CSV file is expected to be the names of the columns.
    :param datafile: path of the file
    :return: a NumPy array containing a data point in each row
    """

    # File format for CSV file. For example, setting _X_COLUMN to 'x' means that x coordinates of geographical location
    # will be at the column named 'x' in the CSV file.
    # This will be useful later when we start adding more features.
    _COLUMN_X = 'x'
    _COLUMN_Y = 'y'
    _COLUMN_W = 'color'

    data = pd.read_csv(datafile)

    # Normalize
    scaler = StandardScaler()
    scaler.fit(data[[_COLUMN_X, _COLUMN_Y]])
    data[[_COLUMN_X, _COLUMN_Y]] = scaler.transform(data[[_COLUMN_X, _COLUMN_Y]])

    data_coords = data[[_COLUMN_X, _COLUMN_Y]].values
    data_words = [[e] for e in data[[_COLUMN_W]].values.flatten().tolist()]

    data = {"coordinates": data_coords, "words": data_words}

    return sparsify_data(data, None, None), scaler  # None for both params since SVD is not used

def prepare_features(data, enc=None, scaler=None):
    '''
    One-hot encode all boolean/string (categorical) features,
    and shift/scale integer/float features
    '''
    # X needs to contain only non-negative integers
    bfs = data['bfeatures'] + 1
    sfs = data['sfeatures'] + 1
    
    # Shift/scale integer and float features to have mean=0, std=1
    ifs = data['ifeatures']
    ffs = data['ffeatures']
    x2 = np.hstack((ifs,ffs))
    if scaler is None:
        scaler = StandardScaler()
        x2 = scaler.fit_transform(x2)
        print "Training features have mean: %s" % scaler.mean_
        print "and standard deviation: %s" % scaler.std_
    else:
        x2 = scaler.transform(x2, copy=False)
        
    # one-hot encode categorical features
    X = np.hstack((bfs,sfs,x2))
    categorical = np.arange(bfs.shape[1]+sfs.shape[1])
    if enc is None:
        enc = OneHotEncoder(n_values='auto', categorical_features=categorical)
        X = enc.fit_transform(X)
        print "One-hot encoded features have dimension %d" % X.shape[1]
    else:
        X = enc.transform(X)
    return X, enc, scaler

def cross_valid(data, classifier, x_cols, y_col, **kwargs):
	# Do train-test split for cross-validation
	size = len(data)
	kf = train_test_split(size)
	y_pred = np.zeros(size)
	y_pred_prob = np.zeros(size)
	y = data[y_col].as_matrix().astype(np.float)
	totaltime_train = 0
	totaltime_test = 0
	for train_index, test_index in kf:
		# Fill in missing values
		df = data.copy()
		df = fill_missing_median(df, train_index)
		# Transform and normalize
		X = df[x_cols].as_matrix().astype(np.float)
		scaler = StandardScaler()
		X = scaler.fit_transform(X)
		# Build classifier and yield predictions
		y_pred[test_index], y_pred_prob[test_index], train_time, test_time \
		= model(X, y, train_index, test_index, classifier, **kwargs)
		totaltime_train += train_time
		totaltime_test += test_time
	avgtime_train = train_time/len(kf)
	avgtime_test = test_time/len(kf)
	return y, y_pred, y_pred_prob, avgtime_train, avgtime_test

def linregress(X_train, X_test, y_train, y_test):
    coef = []
    for col in X_train.columns.tolist():
        X = StandardScaler().fit_transform(X_train[col])
        lr = LinearRegression()
        lr.fit(X.reshape(-1, 1), y_train)
        coef.append([col, lr.coef_])
    coef = sorted(coef, key=lambda x: x[1])[::-1]
    nos = [x[1] for x in coef]
    labs = [x[0] for x in coef]
    for lab in labs:
        if lab == 'doubles':
            labs[labs.index(lab)] = '2B'
        elif lab == 'triples':
            labs[labs.index(lab)] = '3B'
        elif lab == 'Intercept':
            idx = labs.index('Intercept')
            labs.pop(idx)
            nos.pop(idx)
    labs = [lab.upper() for lab in labs]
    x = range(len(nos))
    plt.plot(x,nos, lw=2, c='b')
    plt.xticks(x, labs)
    plt.title('Linear Regression Coefficients (Win Percentage)')
    plt.savefig('images/coefficients.png')
    plt.show()
    print labs